<![CDATA[Students often face difficulties in solving math problems due to high cognitive load. This load can interfere with optimal information processing, particularly at the micro-level, such as in problem-solving steps. Although many studies have examined cognitive load, most focus on macro-level, with limited exploration of micro-level cognitive processing. To address this, an effective learning approach is needed to optimize students’ working memory capacity and promote knowledge transfer. This study aims to investigate the effect of an example-based learning model on cognitive load and knowledge transfer in mathematics learning. A quasi-experimental method was conducted involving 78 eighth-grade students from a school in Serang City, divided into two groups: an experimental group applying the example-based learning model and a control group using a problem-solving model. Data were collected using a mental effort rating scale and essay questions to measure cognitive load at each problem-solving step, along with retention and near-transfer tests. Analysis using Two-Way ANOVA showed that the example-based learning model significantly reduced cognitive load throughout the problem-solving stages. It also produced better outcomes in retention and near-transfer tests, indicating more effective knowledge transfer. These findings suggest that example-based learning can be a valuable instructional strategy to improve mathematical problem-solving, particularly for students with limited background knowledge. The novelty of this study rests on the simultaneous examination of retention and transfer, focusing on students' micro-level cognitive processing during example-based learning. Structured examples were shown to reduce cognitive burden while fostering transferable problem-solving strategies.]]>
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